Java / Python Developer → AI Engineer (Switch Roadmap)
This roadmap is built for one specific situation: you have 2+ years of production engineering experience (Java, Python, .NET, or similar backend stack) and you want to switch into AI / Gen AI engineering in India without taking a salary hit during the transition. You are NOT a beginner. You are an experienced engineer adding a new specialisation.
Duration
6 months · part-time
Difficulty
Intermediate (assumes engineering experience)
Starting salary
₹14–28 LPA after switch (vs ₹8–18 LPA before)
Time commitment
12 hours / week (evenings + weekends)
What does a AI / Gen AI Engineer actually do?
You become an AI Engineer or Gen AI Engineer — building production systems that USE LLMs to solve business problems. Your engineering background is what gets you into rooms where pure-research candidates do not. Your task is to apply standard production engineering (testing, observability, deployment, cost control) to LLM-powered systems.
The good news: your engineering fundamentals — testing, deployment, debugging, system design, code review — transfer cleanly to AI engineering. Most Indian AI hires that get rejected at staff level are weak on production engineering. You start with that as your baseline. What you need to add is LLM API fluency, RAG, agents, evals, and the judgement to know when to prompt vs fine-tune.
Six months is realistic at 12 hours / week. The compressed version of this roadmap (the paid Nettms Gen AI cohort) does it in 3 months at 20+ hours / week. The trade is calendar time vs intensity. Indian product companies care more about the portfolio than the duration on it — what they want is one shipped LLM product, evaluated rigorously, deployed visibly.
Month-by-month plan
The 4-stage path
- 01 · Month 1Salary by end of stage: Current salary
LLM API fluency
Skills to learn
- OpenAI + Claude APIs deep dive
- Prompting patterns
- Function calling / tool use
- Structured outputs
Tools you'll touch
- OpenAI Python SDK
- Anthropic Python SDK
- Pydantic
Projects to build
- Migrate one of your existing tools (a CLI / cron job / report) to use Claude for one step — keep the rest of your stack
Jobs to target
- · Still your current role
- 02 · Month 2–3Salary by end of stage: Current salary + potential bonus
RAG + production
Skills to learn
- Embeddings
- Vector stores (pgvector if you know Postgres, else Pinecone)
- Hybrid search
- RAG patterns
Tools you'll touch
- LangChain
- pgvector / Pinecone
Projects to build
- Build a production-grade RAG over your company's internal docs (with permission) or a public corpus you care about
Jobs to target
- · Internal AI projects at current company
- 03 · Month 4–5Salary by end of stage: Apply for ₹18–28 LPA at this stage
Agents + evals
Skills to learn
- LangGraph state machines
- LangSmith tracing
- Custom Python evals
- Cost + latency engineering
Tools you'll touch
- LangGraph
- LangSmith
Projects to build
- Multi-agent workflow with eval harness + cost tracking — the showcase project for your job applications
Jobs to target
- · Start applying — internal AI roles at current company, AI startups, product companies
- 04 · Month 6Salary by end of stage: Median offer ₹18 LPA, top end ₹28 LPA in 2025 cohort
Interview prep + offer
Skills to learn
- Gen AI system design — 5 reference problems
- Live coding mocks
- Resume rewrite around LLM projects
Tools you'll touch
- GitHub showcase
Projects to build
- Polish your portfolio + write a public Medium / LinkedIn post about your agent eval methodology
Jobs to target
- · AI / Gen AI Engineer at product companies
The exact stack — and why each one matters
OpenAI + Anthropic APIs
The two providers every Indian AI team uses
LangChain + LangGraph
Production frameworks; faster than building from scratch
LangSmith
Evals + tracing — the senior-level skill that separates offers
Vector store of choice
pgvector if you know Postgres, Pinecone if you do not
Your existing stack
Java / Python / TypeScript — keep using it; this is an addition
Build these. Recruiters open them.
- 01RAG over a real-world corpus (your company docs / public legal text / etc.)
- 02Multi-agent workflow with eval harness and cost tracking
- 03Migration of an existing system to use an LLM in one critical step
Where this path leads
- Switch year: AI Engineer at ₹14–28 LPA (median ₹18 LPA in 2025 cohort)
- Year 2: Senior AI Engineer at ₹28–40 LPA
- Year 4–6: Lead AI Engineer / Founding Engineer · ₹50–80+ LPA
Five things people do wrong on this path
- 1Quitting your job before the switch lands — keep paying yourself while you learn
- 2Building toy projects instead of integrating LLMs into your real work
- 3Skipping evals — this is the senior-level differentiator
- 4Hiding your engineering tenure on your resume — it is your competitive edge
- 5Underselling — you are NOT a fresher; do not accept fresher salary
Compress this into a 3-month cohort
Self-paced is free. A structured cohort with weekly mentor reviews + 50-partner placement support compresses the timeline and removes the common failure modes. Same content, faster outcome.
- Live cohort, max 15 students
- Weekly mentor reviews + project feedback
- 90-day placement support · 50+ hiring partners
- 3-month no-cost EMI · 7-day refund